Deep Transfer Learning for Food Recognition

Abdulah Pehlic, Ali Abd Almisreb, Melisa Kunovac, Elmedin Skopljak, Merjem Begovic


Food Recognition is an essential topic in the area of computer of its target applications is to avoid achieving a cashier at the dining place. In this paper, we investigate the application of Deep Transfer Learning for food recognition. We fine-tune three well learning models namely; AlexNet, GoogleNet, and Vgg16. The fine tuning procedure depends on removing the last three layers of each model and adds another five new layers. The training and validation of each model conducted through food a dataset collected from our university's canteen. The dataset contains 39 food types, 20 images for each type. The fine-tuned models show similar training and validation performance and achieved 100% accuracy over the small-scale dataset.


Deep Transfer Learning; Food Recognition; GoogleNet; AlexNet; Vgg16.

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Copyright (c) 2019 Abdulah Pehlic

ISSN 2233 -1859

Digital Object Identifier DOI: 10.21533/scjournal

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License